Unraveling the genetic basis of common polygenic diseases, such as hypertension, diabetes and heart failure, will require fresh approaches to view how genes work together in groups rather than singly. In this proposal, we investigate gene network analysis as a promising new approach. Our goal is to identify specific expression patterns of gene modules, rather than single genes, which predict susceptibility to heart failure (HF). A network analysis of DNA microarray data typically groups 20,000 genes into 20-30 modules, each containing 10's to 100's of gene, drastically reducing number of possible candidates required to perform a gene network- based Gene Module Association Study (GMAS), which will be complementary to GWAS. To test the GMAS concept, we will use a systems genetics approach integrating DNA microarray analysis with physiological studies and computational modeling, to examine whether gene module expression patterns predict susceptibility to heart failure (HF) induced by cardiac stress. For this purpose, we will utilize a novel resource developed at UCLA, the Hybrid Mouse Diversity Panel (HMDP), consisting of 102 strains of inbred mice from which a common mouse cardiac modular gene network comprised of 20 gene modules has been constructed. Our preliminary findings reveal that different HMDP strains show considerable variability in both gene module expression patterns and phenotypic response to chronic cardiac stress (isoproterenol). Using biological and computational experiments, we will test the hypothesis that gene module expression patterns among HMDP strains represent different good enough solutions, all of which are adequate for normal excitation-contraction- metabolism coupling, but have different abilities to adapt to chronic cardiac stress. Three Specific Aims integrating experimental and computational biology and combining discovery-driven, hypothesis-driven, and translational elements are proposed, towards the goal of relating HMDP results directly to human heart failure.